{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61f7845d-e844-453a-813f-5c7a68c132e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡头酒数据如下:\n",
      "      label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = ['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv('wine.data',names=names)\n",
    "print('葡头酒数据如下:\\n',dataset)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e12283b-c3dc-4014-a07f-708689b32a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1 中异常值 []\n",
      "a 2 中异常值 [5.8 5.51 5.65]\n",
      "a 3 中异常值 [1.36 3.22 3.23]\n",
      "a 4 中异常值 [10.6 30.0 28.5 28.5]\n",
      "a 5 中异常值 [151.0 139.0 136.0 162.0]\n",
      "a 6 中异常值 []\n",
      "a 7 中异常值 []\n",
      "a 8 中异常值 []\n",
      "a 9 中异常值 [3.28 3.58]\n",
      "a 10 中异常值 [10.8 13.0 11.75 10.68]\n",
      "a 11 中异常值 [1.71]\n",
      "a 12 中异常值 []\n",
      "a 13 中异常值 []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "data.plot(kind='box',subplots=True,layout=(3,5),sharex=False,sharey=False)\n",
    "p=data.boxplot(return_type='dict')\n",
    "for i in range(13):\n",
    "    y=p['fliers'][i].get_ydata()\n",
    "    print('a',i+1,'中异常值',y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf5e63b6-1a66-466d-b43c-ec10aa68142d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.5622498   0.23205254 ...  0.36217728  1.84791957\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.49941338 -0.82799632 ...  0.40605066  1.1134493\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.02123125  1.10933436 ...  0.31830389  0.78858745\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.74474449 -0.38935541 ... -1.61212515 -1.48544548\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.22769377  0.01273209 ... -1.56825176 -1.40069891\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.58316512  1.36520822 ... -1.52437837 -1.42894777\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn import preprocessing\n",
    "names = ['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv('wine.data',names=names)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)].values.reshape(1,178)[0]\n",
    "cdata=preprocessing.StandardScaler().fit_transform(data)\n",
    "print(cdata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0056e3e-a4bb-4d28-8f37-20f4b38caa04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
